Data visualisations

the ausdex module comes with several data visulisations out of the box build with plotly.

Visualisations can be created via the command line and saved as html files, or created programmatically.

Inflation data visualisations

Inflation data can be visulised by a time series of the values of a given price inflated to a particular “dollar year”. Additionally, the consumer price index (CPI) time series can be plotted.

Inflation visualisations

Inflation visulisations can be created from the command line, and via the python api.

Via the command line:

an inflation chart can be created and saved as an html file via the following command:

ausdex plot-inflation [OPTIONS] COMPARE_DATE OUT

With the following arguments and options:

• COMPARE_DATE (str): Date to set relative value of the dollars too.

• OUT (Path): Path to html file where plot will be saved. options:

• --start-date: (Union[datetime, str, None], optional): Date to set the beginning of the time series graph. Defaults to None, which starts in

• --end_date: (Union[datetime, str, None], optional): Date to set the end of the time series graph too. Defaults to None, which will set the end date to the most recent quarter.

• value: (Union[float, int], optional): Value you in compare_date dollars to plot on the time series. Defaults to 1.

Via the python api

The same chart can be created with the same arguments programmatically

from ausdex.inflation import plot_inflation_timeseries

UserWarning: The Shapely GEOS version (3.8.0-CAPI-1.13.1 ) is incompatible with the GEOS version PyGEOS was compiled with (3.9.1-CAPI-1.14.2). Conversions between both will be slow.

fig = plot_inflation_timeseries('12-12-2019', start_date='12-12-1950', end_date='12-06-2020')

Downloading https://www.abs.gov.au/statistics/economy/price-indexes-and-inflation/consumer-price-index-australia/dec-2021/640101.xls to /Users/garberj/Library/Caches/ausdex/640101-dec-2021.xls

CPI data for 2022-01-27 12:23:21.522936 not available.

fig.show()

CPI visualisations

The time series of the CPI can be created as an html file via the command line, or programmatically via the python api.

Via the command line

Using the following commands, a time series fo the CPI can be generated.

an inflation chart can be created and saved as an html file via the following command:

ausdex plot-cpi [OPTIONS] OUT

With the following arguments and options:

• OUT (Path): Path to html file where plot will be saved. options:

• --start-date: (Union[datetime, str, None], optional): Date to set the beginning of the time series graph. Defaults to None, which starts in

• --end_date: (Union[datetime, str, None], optional): Date to set the end of the time series graph too. Defaults to None, which will set the end date to the most recent quarter. #### Via the python api The same plot can be made programmatically via the python api

from ausdex.inflation import plot_cpi_timeseries

fig_cpi = plot_cpi_timeseries(start_date=1960, end_date='12-12-2016')

fig_cpi.show()

Seifa_vic visualisations

Map visualisations, as well as time series of selected suburbs can be created programmatically or via the command line.

Map visualisations

Plotly choropleth maps of suburb interpolated seifa vic metrics can be created via a given year on the command line and via the python api.

On the command line:

ausdex seifa-vic-map [OPTIONS] DATE METRIC:[ier_score|irsd_score|ieo_sc
                            ore|irsad_score|rirsa_score|uirsa_score] OUT

with the following inputs:

• DATE (int, float, str): Year values in decimal years or in a string datetime format convertable by pandas.to_datetime function

• METRIC: is the name of the metric to interpolate:

  • irsd_score for index of relative socio economic disadvantage,

  • ieo_score for the index of education and opportunity,

  • ier_score for an index of economic resources

  • irsad_score for index of socio economic advantage and disadvantage,

  • uirsa_score for the urban index of relative socio economic advantage,

  • rirsa_score for the rural index of relative socio economic advantage

• OUT (Path): Path to save html graph to.

options: - --fill-value (str, optional): optional input that dictates how the data are extrapolated outside the year range of the dataset. default is null which returns np.nan values. Other options include extrapolate, which extrapolates the data, boundary_value which takes the nearest data point, and options defined in the scipy.interpolate.interp1d fill_value optional argument. - --min-x (float, optional): minimum x coordinate boundary of intersecting polygons to plot. Defaults to None. - --min-y (float, optional): maximum x coordinate boundary of intersecting polygons to plot. Defaults to None. - --max-x (float, optional): minimum y coordinate boundary of intersecting polygons to plot Defaults to None. - --max-y (float, optional): maximum y coordinate boundary of intersecting polygons to plot. Defaults to None. - --clip-mask (Path, optional): path to mask polygon data to clip the dataset to, overrides min_x, max_x, min_y, max_y. Defaults to None.

Via the python api

The same graph can be created via the python api with an additoinal argument:

• simplify (float, optional): This argument simplifies the geometries using the simplify value as the tolerance input in geopandas.GeoSeries.simplify. Defaults to 0.001

from ausdex.seifa_vic import get_seifa_map

fig_map = get_seifa_map('12-12-2015', 'ier_score', fill_value='extrapolate', max_y = -37.45, simplify= 0.01)

/Users/garberj/opt/anaconda3/envs/aucpi_env/lib/python3.7/site-packages/geopandas/geodataframe.py:1351: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

fig_map.show()

Plotting SEIFA time series:

In addition to maps of individual times, a time series can be plotted for selected suburbs via the command line, and programmatically via the python api.

Via the command line

SIEFA metric time series can be plotted using the following command:

ausdex seifa-vic-plot [OPTIONS] METRIC:[ier_score|irsd_score|ieo_score|
                             irsad_score|rirsa_score|uirsa_score] OUT
                             SUBURBS...

with the following inputs and options:

• METRIC (Union[Metric, str]): metric to plot along the time series.

  • irsd_score for index of relative socio economic disadvantage,

  • ieo_score for the index of education and opportunity,

  • ier_score for an index of economic resources

  • irsad_score for index of socio economic advantage and disadvantage,

  • uirsa_score for the urban index of relative socio economic advantage,

  • rirsa_score for the rural index of relative socio economic advantage

• OUT (Path): Path to html file where plot will be saved

• SUBURBS (list): list of suburbs to include in the time series

Via the python api

The same plot can be created programmatically via the python api

from ausdex.seifa_vic import create_timeseries_chart

fig_seifa_time = create_timeseries_chart(['abbotsford', 'footscray'], 'ier_score')

fig_seifa_time.show()